The Self-Organizing Networks (SON) concept includes the functional area known as self-healing, which aims to automate the detection and diagnosis of, and recovery from, network degradations and outages. In this paper, we build on our previous work [19] and study the feasibility of an operational deployment of an adaptive ensemble-method framework for modeling cell behavior. The framework uses Key Performance Indicators (KPIs) to determine cell-performance status. Our results, generated using real cellular network data, show that the computational overhead and the detection delay are sufficiently low for practical use of our methods to perform cell anomaly detection in operational networks.